Making sense of complex systems
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First delivered as a guest speaker at Mark Esposito's System & Complexity Thinking course at Hult University, June 2021. This talk is aimed at people learning about complex systems with a background in organisational design & management. It looks at the difference between Computational Complexity and Applied Social Complexity, touches on the limits of Computation, Business Processes, & Systems Thinking, and introduces two frameworks that can be used to help organisations understand their current context and navigate more effectively. Most content in the notes.
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Making sense of complex systems
- 1. Making sense of complex systems Hult, June 2021 by Steve Purkis mages: pixabay.com
- 2. Image: pixabay.com/illustrations/fractal-art-3d-fractals-3206739/ It all started with a speech
- 3. Making sense of complex systems
- 4. image: Brian Castellani, 2018 art-sciencefactory.com/complexity-map_feb09.html what the… ???
- 5. image: Brian Castellani, 2018 art-sciencefactory.com/complexity-map_feb09.html My early introduction: computational perspective.
- 7. Aistis Simaitis image © Aistis Simaitis
- 8. Sandra Chapman, University of Warwick Image: wikimedia contributors
- 9. image © Aleria Paolo Gaudiano
- 13. Image: https://pixabay.com/photos/beach-summer-blue-summer-beach-2123440/ Storm warning rain / snow: 90% Today’s Forecast 2ºC
- 16. https://commons.wikimedia.org/wiki/File:Social_Network_Analysis_Visualization.png It’s the connections that matter # Relationships = n2
- 17. image: Brian Castellani, 2018 art-sciencefactory.com/complexity-map_feb09.html Dave Snowden “Anthro” Complexity
- 18. How does it fit in? Source: Dave Snowden & Peter Stanbridge “The landscape of management: Creating the context for understanding social complexity” (2004)
- 19. Complex systems are not causal.
- 20. Thinking is slow and expensive image: unlocke.org/neuroscience.php
- 21. R R legend daughter me Seek Attention Whinge R Annoyed Positive Self- Esteem Fed Up Punish Happy Praise Play Fun R ✘ ✘ ✘ (Snowden & Stanbridge, 2004) Systems thinking is a useful tool for making sense of complex systems... But it only gives you the perspectives you put in, and it assumes causality where there may be none. Use it with that in mind.
- 22. KPI’s for outcomes (Snowden & Stanbridge, 2004) Management systems are highly ordered some of the time…
- 23. Directional KPI’s (Snowden & Stanbridge, 2004) We need different approaches for the other times.
- 24. Whitney Houston Image: wikimedia contributors How will I know?
- 25. Dave Snowden Cynefin® Framework image: © Cognitive Edge 1
- 26. It’s obvious what to do. Experts or due process would tell us the right thing to do. Hindsight would tell us the right thing to do, we can’t know in advance. This is random & unexpected, an urgent response is needed! more detail: cynefin.io/wiki/4-points
- 27. Chaos Act → Sense → Respond Complex Probe → Sense → Respond Complicated Sense → Analyse → Respond Clear Sense → Categorise → Respond
- 28. Wardley Maps 2 Value Chain Topographical Business Intelligence Simon Wardley
- 29. Strategy Cycle Climatic Patterns Universal Doctrine Context Specific Gameplay ✘ ✘o ✘o ✘o ✘o ✘o o Maps
- 30. Visibility Evolution What’s a Wardley Map? User Flow of Value Flow of Finance
- 31. Visibility A Cup of Tea Steve
- 32. A quick example
- 33. Evolution Value Chain visible invisible Genesis Custom Product (+ rental) Commodity (+ utility) uncharted industrialised Modal Digital’s Value Chain Modal’s Customer E-commerce Site E-commerce Consulting Hosting (eg: AWS, Azure, Google Cloud) E-commerce Framework User Interface Customisations Third Party Integrations User Experience & Interface Design Bespoke Customisations Technical Support Remote-first workforce Remote-first customers
- 34. data source: Companies House (anonymised to protect identity)
- 35. Evolution Value Chain visible invisible Genesis Custom Product (+ rental) Commodity (+ utility) uncharted industrialised Modal’s Customer E-commerce Site E-commerce Platform as a Service (eg: SFCC, Shopify) competition standards competition & standards Hosting (eg: AWS, Azure, Google Cloud) E-commerce Framework User Interface Customisations future competition Third Party Integrations User Experience & Interface Design product add-ons Bespoke Customisations E-commerce Consulting Technical Support Modal Digital’s Value Chain Already under threat! Inertia
- 37. Evolution Value Chain visible invisible Genesis Custom Product (+ rental) Commodity (+ utility) uncharted industrialised Modal’s Customer E-commerce Site E-commerce Platform as a Service (eg: SFCC, Shopify) Hosting (eg: AWS, Azure, Google Cloud) E-commerce Framework User Interface Customisations Third Party Integrations User Experience & Interface Design Bespoke Customisations E-commerce Consulting Technical Support Modal Digital’s Value Chain COVID-19 forcing function ✘ ✘
- 38. Complicated Clear Complex Chaos Modal’s Business Model The Cliff of Complacency adaptation extinction cliff of complacency exaptation
- 39. Evolution Value Chain visible invisible Genesis Custom Product (+ rental) Commodity (+ utility) uncharted industrialised Modal’s New Customer User Interface Customisations Third Party Integrations User Experience & Interface Design Bespoke Customisations Technical Support Modal Digital’s Value Chain ? ? Exaptation Decompose to find novel uses ✘ ✘
- 40. Evolution Value Chain visible invisible Genesis Custom Product (+ rental) Commodity (+ utility) uncharted industrialised Coherent Probes Modal’s New Customer User Interface Customisations Third Party Integrations User Experience & Interface Design Bespoke Customisations Technical Support Modal Digital’s Value Chain ? ? ✘ ✘
- 41. Organised Services Operating Model vs Functional Hierarchy James Duncan Service Manager design, delivery, operations, finance, ... Container Service Service Owner Executive strategy, governance Security Scan Service Web Service Sub-teams as needed Service Contract Compute Service Service Mandates https://osom.guide/
- 42. Wrapping up
- 44. Recommendations (see slides for other links) Cynefin ● Cynefin introduction (8min video) ● A Leader's Framework for Decision Making (HBR Article, 2007) ● Cynefin.io (practitioner’s wiki) ● Cognitive Edge Apex Predator / Flexuous Curves (not covered, but very topical) ● Evolving Naval Safety: An Apex Predator Approach Wardley Maps: ● Humorous Wardley Maps intro (20min video) ● Book: medium.com/wardleymaps ● Steve’s Introduction to Wardley Maps, and example of real-world use: Wardley Maps @ Glasswall ● LearnWardleyMapping.com ● Organised Services Operating Model Guide Conferences ● International Conference on Complex Systems (ICCS) ● Map Camp 2021 ● Complex Systems Society (caveat: I’ve not been yet) Complexity & Systems Thinking (sites, pdf’s, videos) ● Complexity concept map (Yaneer Bar-Yam) ● Systems-thinking: A Little Film (12 min) ● John Boyd’s OODA Loop ● This simple equation will change how you see the world - Veritasium (20min video) Related books: ● Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Miller & Page) ● Chaos (James Gleik) ● Order out of Chaos (Ilya Prigogine) ● Knowledge Assets (Max Boisot) ● Cynefin® book (Dave Snowden et al)
Editor's Notes
- I first started out with the topic of complexity when I was a kid... If you remember back to your highschool days where they get you up in front of the class to do a speech? Well, I did one about Fractals, I just remember being amazed that you could create things that looked like coastlines no matter how much you zoomed in… and then, if you look at our coastline from space and zoom in, it’s the same thing! That got me interested in Chaos theory, finding order where there seemingly was none, and eventually to software agents and artificial neural networks. Anyways, we’re here to talk complexity, not hear my life story ;-)
- I want to talk about making sense of complex systems, and I don’t want this to be overly academic, so I’m going to throw in real-life examples to show how this stuff is really useful. Now, it’s day 3 of your course, so Mark’s hopefully already explained what a complex system is ;-). To recap:
- Well, it’s complicated. Despite it being a relatively young field, there’s already some 50 different areas of complexity science highlighted in Brian Castellani’s diagram here.
- My early introduction came from a computational complexity perspective, and that’s probably the one you’ve heard most about.
- Made popular by The Santa Fe institute, and the New England Complex Systems Institute or NECSI, and more recently by the growing field of Big Data & Artificial Intelligence… you can think of computational complexity as agent based models and mathematical simulations. At last year’s International Conference on Complex Systems, I was impressed to see how a number of people were using agent based models.
- Aistis from EndCoronavirus.org used them to simulate the spread of covid-19. While we all hated being cooped up in lockdowns and wearing masks when we go out, models like this have literally saved millions of lives.
- Sandra Chapman showed how her team was connecting magnetometers around the world to allow us to better predict when space weather will cause havoc with our power grids and IT systems. Forewarned is forearmed!
- And Paolo Gaudiano’s organisation, Aleria, uses them to show the impact of gender bias. Aleria’s work shows how this problem is systemic, and even better, what to do about it. This diversity is critical to cultivate for complex social systems, as we’ll see in a moment.
- I’ve used computational complexity myself... last year as part of our Hult Business Challenge, I helped a company called X-Zell to create a prototype of a deep learning algorithm to detect cancer in blood samples by highlighting certain rare cells to pathologists. They have gone on to productise this in their quest for zero deaths from cancer. There is clear social & business value in this!
- I’ve also created computer games with basic NPCs that result in gameplay you couldn’t predict. There’s a big market there too.
- While powerful, computational complexity has its limits: First, it is deeply technical and so the barrier to entry is high. Data visualization techniques have really improved the accessibility of these models… we all know that a picture says a thousand words!
- Second, and perhaps more importantly, it is limited by the extent to which the model actually represents reality. That in turn is limited by things like: the rules & algorithms used by the models the time & effort put into creating the models the capabilities & biases of the people creating the models the intentions & biases of the organisations creating the models the data used to build & operate the models and our ability to interpret the models
- Computation offers a very valuable perspective, but it’s only one perspective. If I’ve learned anything, it’s that you need to actively seek out multiple diverse perspectives to make sense of & navigate complex adaptive systems.
- As you probably know by now, it’s not just shoals of fish, birds, ants, or honeybees... Humans also form complex adaptive systems wherever we go. Our markets, economies, businesses, governments, charities, teams, friends and families all form complex adaptive social systems. These all show the telltale signs of complexity: A high number of interacting agents With emerging patterns of behaviour That can’t be predicted And adapt to changing circumstances With non-linear responses
- The complexity is in the connections between people, how we interact, our shared values & beliefs, our goals, and how we live day-to-day. In business terms, it’s reflected by our cultures, our vision & mission statements, our strategies & tactics, our policies & procedures, our reward systems, and our daily activities. Consider this: If you were on a team of 10 people, there’d be 100 interpersonal relationships. Add in 6 stakeholders, and you’re at 256. Trying to predict what any individual will do in a given circumstance is a complex problem, as we are each influenced by a myriad of factors not least of which is our interactions with one another and the environments we occupy
- And that’s where applied social complexity comes in.
- You might be thinking: “how does this fit in with the other stuff I’m learning this weekend?” Well, I think this diagram does a good job of putting it into perspective. It plots management approaches on two axes: Y: is the approach ordered or un-ordered? X: is the approach based on concrete rules? Or is it based on practical judgement, heuristics (rules-of-thumb) Note the placement of systems thinking, which assumes order. That’s because it assumes causality, and that’s a fundamental point of difference: cause and effect cannot be known in advance in a complex system.
- That’s right, complex systems are not causal. At best, you can glean some relationship after the fact. But even that “retrospective coherence” can be tenuous and dangerous if taken as truth. And that really grates on people, because we seek order and we fear uncertainty.
- We want to know, and we want things to be simple. In fact, we’re wired for it, as Kahneman describes in Thinking Fast & Slow.
- Don’t get me wrong, systems thinking is a really useful tool to make sense of complex systems. But my point is, like computational complexity, it only gives you the perspectives you put in, and it assumes causality where there may be none. You just need to interpret it in that context.
- I highlight this, because this is one of the biggest stumbling blocks I see. I meet many business leaders who have grown up under Taylor’s principles of Scientific Management and expect their business to exist solely in highly-ordered systems that they can control. Here, KPIs for outcomes can be set because they are known in advance. Sadly, this is only true some of the time.
- In a complex adaptive system, Dave Snowden teaches us to set directional KPIs, as outcomes cannot be planned, and your actions may change the landscape invalidating what you thought you wanted. You can think of these as experiments or probes.
- The problem is, how do you know what kind of system you’re currently in? Attending a course like this will certainly help you! There are also two really useful tools for this kind of situational awareness.
- The first sense-making tool is Dave Snowden’s Cynefin framework. Getting started is really simple.
- Next time you’re feeling stuck, grab a colleague and put these 4 sentences up on the corners of a whiteboard. Then write whatever it is you’re thinking about on a sticky note, and discuss where it fits. Think of the corners as attractors. It’s ok if your sticky gets stuck halfway between different corners, that’s normal. The point is to think about it and use your judgement to find the right place.
- When you’ve done that, you can add in the Cynefin framework back in, and use it to help you decide what to do: If you find yourself in Complicated or Clear, then reach for those familiar outcome-based KPIs and detailed project plans. If you find yourself in Complex, think about what experiments or probes you should run in parallel. If you’re in Chaos, well, put out the fire before doing anything else! It gets more nuanced than that, of course, but even this level of usage can help you make better decisions!
- The second sense-making tool is Simon Wardley’s value chain mapping technique, and it may be a bit reminiscent to Systems Thinking.
- Created by Simon Wardley over a decade ago because he found the prevailing business strategy tools lacking.
- Briefly, wardley maps are: A chain of Needs Drawn from the perspective of a User (more visible @ top) Each link represents some flow of value (eg: needs met, finance, energy, risk, CO2, etc.) With the added dimension of Evolution (newer @ left)
- One of the most useful things about a map is that we can then have a conversation about it You might disagree with me, for example: Homemade Mugs are not “Custom” Sure, the design your daughter made was custom But the actual mug, paints, and baking process is a product These discussions help groups of people examine multiple perspective to make sense of things.
- Here’s a quick example, of a real company’s journey over the past year.
- Modal Digital is an Ecommerce Solutions Integrator that, on the surface, looked well-equipped to weather the storm
- (But underneath it all, were actually suffering without really knowing why)
- When you map, you realise they are suffering from inertia. Modal resisted the shift to Ecommerce platforms. Why? Successful business Using PaaS meant lower revenues: No hosting or support? Greatly reduced need for bespoke? No thanks! Plus, what would we do with the capability we’ve built up?
- These market shifts had early signals in 2015, and became obvious in 2016-2017.
- Unfortunately, they experienced the Covid forcing function. Accelerating the change. Their customers still needed to sell But they weren’t looking to spend a lot It’s loss mitigation behaviour, and the platforms offer a better deal
- Modal is one of many organisations that were forced over the cliff of complacency The unfortunate ones go extinct, The lucky ones like Modal took quick action to stop the bleeding. And found themselves in a complex situation they didn’t fully understand. The sensible ones cast about with multiple probes that may: Prompt exaptation, reusing existing capabilities for things they weren’t really meant for (Liz’s dinosaurs + feathers example) Or they may spark a process of adaptation. ------------------ exaptation: reusing capabilities for purposes they weren’t really planned for adaptation: produced by natural selection for its current function
- Exaptation can be the quickest route to recovery, and these Maps can help us do that. They can help us breakdown our capabilities and look for novel uses, so we can recombine them into new products & services.
- You can use maps like this to do scenario planning. They act as coherent probes which you can use to find new opportunities. BTW: Modal found their feet by downsizing and switching focus to User Experience.
- These maps can also be used to create adaptive organisational structures. The best example I’ve seen of this is Stance’s “Organised Services Operating Model”. Rather than using a classic functional hierarchy OSOM breaks up organisations into cells that deliver a Service Each cell has a contract which details the services provided, and a service level agreement This doubles as a fitness function to evaluate performance of the service The Executive decides which services to spin up, which to spin down. It also governs via mandates, rather than dictats This is all based on Wardley Maps: in fact, it’s derived from Simon’s Universal Doctrine.
- At any rate, I’ve got loads more examples I could show you, but I don’t want to go too deep without leaving any time for discussion & questions. So to sum up: There’s 2 main approaches I’ve seen to complex systems: computation, and applied social Seeking out multiple diverse perspectives are key to making sense of and navigating complex adaptive systems Focus on increasing your situational awareness Tools like Systems Thinking, Wardley Maps, and Cynefin can help you do that
- If you found this session useful, or if you have any feedback please let me know!